#ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED
#define NNUE_FEATURE_TRANSFORMER_H_INCLUDED
-#include "nnue_common.h"
+#include <algorithm>
+#include <cassert>
+#include <cstdint>
+#include <cstring>
+#include <iosfwd>
+#include <utility>
+
+#include "../position.h"
+#include "../types.h"
+#include "nnue_accumulator.h"
#include "nnue_architecture.h"
-
-#include <cstring> // std::memset()
-#include <utility> // std::pair
+#include "nnue_common.h"
namespace Stockfish::Eval::NNUE {
- using BiasType = std::int16_t;
- using WeightType = std::int16_t;
- using PSQTWeightType = std::int32_t;
-
- // If vector instructions are enabled, we update and refresh the
- // accumulator tile by tile such that each tile fits in the CPU's
- // vector registers.
- #define VECTOR
-
- static_assert(PSQTBuckets % 8 == 0,
- "Per feature PSQT values cannot be processed at granularity lower than 8 at a time.");
-
- #ifdef USE_AVX512
- using vec_t = __m512i;
- using psqt_vec_t = __m256i;
- #define vec_load(a) _mm512_load_si512(a)
- #define vec_store(a,b) _mm512_store_si512(a,b)
- #define vec_add_16(a,b) _mm512_add_epi16(a,b)
- #define vec_sub_16(a,b) _mm512_sub_epi16(a,b)
- #define vec_mul_16(a,b) _mm512_mullo_epi16(a,b)
- #define vec_zero() _mm512_setzero_epi32()
- #define vec_set_16(a) _mm512_set1_epi16(a)
- #define vec_max_16(a,b) _mm512_max_epi16(a,b)
- #define vec_min_16(a,b) _mm512_min_epi16(a,b)
- inline vec_t vec_msb_pack_16(vec_t a, vec_t b){
- vec_t compacted = _mm512_packs_epi16(_mm512_srli_epi16(a,7),_mm512_srli_epi16(b,7));
+using BiasType = std::int16_t;
+using WeightType = std::int16_t;
+using PSQTWeightType = std::int32_t;
+
+// If vector instructions are enabled, we update and refresh the
+// accumulator tile by tile such that each tile fits in the CPU's
+// vector registers.
+#define VECTOR
+
+static_assert(PSQTBuckets % 8 == 0,
+ "Per feature PSQT values cannot be processed at granularity lower than 8 at a time.");
+
+#ifdef USE_AVX512
+using vec_t = __m512i;
+using psqt_vec_t = __m256i;
+ #define vec_load(a) _mm512_load_si512(a)
+ #define vec_store(a, b) _mm512_store_si512(a, b)
+ #define vec_add_16(a, b) _mm512_add_epi16(a, b)
+ #define vec_sub_16(a, b) _mm512_sub_epi16(a, b)
+ #define vec_mul_16(a, b) _mm512_mullo_epi16(a, b)
+ #define vec_zero() _mm512_setzero_epi32()
+ #define vec_set_16(a) _mm512_set1_epi16(a)
+ #define vec_max_16(a, b) _mm512_max_epi16(a, b)
+ #define vec_min_16(a, b) _mm512_min_epi16(a, b)
+inline vec_t vec_msb_pack_16(vec_t a, vec_t b) {
+ vec_t compacted = _mm512_packs_epi16(_mm512_srli_epi16(a, 7), _mm512_srli_epi16(b, 7));
return _mm512_permutexvar_epi64(_mm512_setr_epi64(0, 2, 4, 6, 1, 3, 5, 7), compacted);
- }
- #define vec_load_psqt(a) _mm256_load_si256(a)
- #define vec_store_psqt(a,b) _mm256_store_si256(a,b)
- #define vec_add_psqt_32(a,b) _mm256_add_epi32(a,b)
- #define vec_sub_psqt_32(a,b) _mm256_sub_epi32(a,b)
- #define vec_zero_psqt() _mm256_setzero_si256()
- #define NumRegistersSIMD 32
- #define MaxChunkSize 64
-
- #elif USE_AVX2
- using vec_t = __m256i;
- using psqt_vec_t = __m256i;
- #define vec_load(a) _mm256_load_si256(a)
- #define vec_store(a,b) _mm256_store_si256(a,b)
- #define vec_add_16(a,b) _mm256_add_epi16(a,b)
- #define vec_sub_16(a,b) _mm256_sub_epi16(a,b)
- #define vec_mul_16(a,b) _mm256_mullo_epi16(a,b)
- #define vec_zero() _mm256_setzero_si256()
- #define vec_set_16(a) _mm256_set1_epi16(a)
- #define vec_max_16(a,b) _mm256_max_epi16(a,b)
- #define vec_min_16(a,b) _mm256_min_epi16(a,b)
- inline vec_t vec_msb_pack_16(vec_t a, vec_t b){
- vec_t compacted = _mm256_packs_epi16(_mm256_srli_epi16(a,7), _mm256_srli_epi16(b,7));
+}
+ #define vec_load_psqt(a) _mm256_load_si256(a)
+ #define vec_store_psqt(a, b) _mm256_store_si256(a, b)
+ #define vec_add_psqt_32(a, b) _mm256_add_epi32(a, b)
+ #define vec_sub_psqt_32(a, b) _mm256_sub_epi32(a, b)
+ #define vec_zero_psqt() _mm256_setzero_si256()
+ #define NumRegistersSIMD 16
+ #define MaxChunkSize 64
+
+#elif USE_AVX2
+using vec_t = __m256i;
+using psqt_vec_t = __m256i;
+ #define vec_load(a) _mm256_load_si256(a)
+ #define vec_store(a, b) _mm256_store_si256(a, b)
+ #define vec_add_16(a, b) _mm256_add_epi16(a, b)
+ #define vec_sub_16(a, b) _mm256_sub_epi16(a, b)
+ #define vec_mul_16(a, b) _mm256_mullo_epi16(a, b)
+ #define vec_zero() _mm256_setzero_si256()
+ #define vec_set_16(a) _mm256_set1_epi16(a)
+ #define vec_max_16(a, b) _mm256_max_epi16(a, b)
+ #define vec_min_16(a, b) _mm256_min_epi16(a, b)
+inline vec_t vec_msb_pack_16(vec_t a, vec_t b) {
+ vec_t compacted = _mm256_packs_epi16(_mm256_srli_epi16(a, 7), _mm256_srli_epi16(b, 7));
return _mm256_permute4x64_epi64(compacted, 0b11011000);
- }
- #define vec_load_psqt(a) _mm256_load_si256(a)
- #define vec_store_psqt(a,b) _mm256_store_si256(a,b)
- #define vec_add_psqt_32(a,b) _mm256_add_epi32(a,b)
- #define vec_sub_psqt_32(a,b) _mm256_sub_epi32(a,b)
- #define vec_zero_psqt() _mm256_setzero_si256()
- #define NumRegistersSIMD 16
- #define MaxChunkSize 32
-
- #elif USE_SSE2
- using vec_t = __m128i;
- using psqt_vec_t = __m128i;
- #define vec_load(a) (*(a))
- #define vec_store(a,b) *(a)=(b)
- #define vec_add_16(a,b) _mm_add_epi16(a,b)
- #define vec_sub_16(a,b) _mm_sub_epi16(a,b)
- #define vec_mul_16(a,b) _mm_mullo_epi16(a,b)
- #define vec_zero() _mm_setzero_si128()
- #define vec_set_16(a) _mm_set1_epi16(a)
- #define vec_max_16(a,b) _mm_max_epi16(a,b)
- #define vec_min_16(a,b) _mm_min_epi16(a,b)
- #define vec_msb_pack_16(a,b) _mm_packs_epi16(_mm_srli_epi16(a,7),_mm_srli_epi16(b,7))
- #define vec_load_psqt(a) (*(a))
- #define vec_store_psqt(a,b) *(a)=(b)
- #define vec_add_psqt_32(a,b) _mm_add_epi32(a,b)
- #define vec_sub_psqt_32(a,b) _mm_sub_epi32(a,b)
- #define vec_zero_psqt() _mm_setzero_si128()
- #define NumRegistersSIMD (Is64Bit ? 16 : 8)
- #define MaxChunkSize 16
-
- #elif USE_MMX
- using vec_t = __m64;
- using psqt_vec_t = __m64;
- #define vec_load(a) (*(a))
- #define vec_store(a,b) *(a)=(b)
- #define vec_add_16(a,b) _mm_add_pi16(a,b)
- #define vec_sub_16(a,b) _mm_sub_pi16(a,b)
- #define vec_mul_16(a,b) _mm_mullo_pi16(a,b)
- #define vec_zero() _mm_setzero_si64()
- #define vec_set_16(a) _mm_set1_pi16(a)
- inline vec_t vec_max_16(vec_t a,vec_t b){
- vec_t comparison = _mm_cmpgt_pi16(a,b);
- return _mm_or_si64(_mm_and_si64(comparison, a), _mm_andnot_si64(comparison, b));
- }
- inline vec_t vec_min_16(vec_t a,vec_t b){
- vec_t comparison = _mm_cmpgt_pi16(a,b);
- return _mm_or_si64(_mm_and_si64(comparison, b), _mm_andnot_si64(comparison, a));
- }
- #define vec_msb_pack_16(a,b) _mm_packs_pi16(_mm_srli_pi16(a,7),_mm_srli_pi16(b,7))
- #define vec_load_psqt(a) (*(a))
- #define vec_store_psqt(a,b) *(a)=(b)
- #define vec_add_psqt_32(a,b) _mm_add_pi32(a,b)
- #define vec_sub_psqt_32(a,b) _mm_sub_pi32(a,b)
- #define vec_zero_psqt() _mm_setzero_si64()
- #define vec_cleanup() _mm_empty()
- #define NumRegistersSIMD 8
- #define MaxChunkSize 8
-
- #elif USE_NEON
- using vec_t = int16x8_t;
- using psqt_vec_t = int32x4_t;
- #define vec_load(a) (*(a))
- #define vec_store(a,b) *(a)=(b)
- #define vec_add_16(a,b) vaddq_s16(a,b)
- #define vec_sub_16(a,b) vsubq_s16(a,b)
- #define vec_mul_16(a,b) vmulq_s16(a,b)
- #define vec_zero() vec_t{0}
- #define vec_set_16(a) vdupq_n_s16(a)
- #define vec_max_16(a,b) vmaxq_s16(a,b)
- #define vec_min_16(a,b) vminq_s16(a,b)
- inline vec_t vec_msb_pack_16(vec_t a, vec_t b){
- const int8x8_t shifta = vshrn_n_s16(a, 7);
- const int8x8_t shiftb = vshrn_n_s16(b, 7);
- const int8x16_t compacted = vcombine_s8(shifta,shiftb);
- return *reinterpret_cast<const vec_t*> (&compacted);
- }
- #define vec_load_psqt(a) (*(a))
- #define vec_store_psqt(a,b) *(a)=(b)
- #define vec_add_psqt_32(a,b) vaddq_s32(a,b)
- #define vec_sub_psqt_32(a,b) vsubq_s32(a,b)
- #define vec_zero_psqt() psqt_vec_t{0}
- #define NumRegistersSIMD 16
- #define MaxChunkSize 16
-
- #else
- #undef VECTOR
-
- #endif
-
-
- #ifdef VECTOR
-
- // Compute optimal SIMD register count for feature transformer accumulation.
-
- // We use __m* types as template arguments, which causes GCC to emit warnings
- // about losing some attribute information. This is irrelevant to us as we
- // only take their size, so the following pragma are harmless.
- #if defined(__GNUC__)
- #pragma GCC diagnostic push
- #pragma GCC diagnostic ignored "-Wignored-attributes"
- #endif
-
- template <typename SIMDRegisterType,
- typename LaneType,
- int NumLanes,
- int MaxRegisters>
- static constexpr int BestRegisterCount()
- {
- #define RegisterSize sizeof(SIMDRegisterType)
- #define LaneSize sizeof(LaneType)
-
- static_assert(RegisterSize >= LaneSize);
- static_assert(MaxRegisters <= NumRegistersSIMD);
- static_assert(MaxRegisters > 0);
- static_assert(NumRegistersSIMD > 0);
- static_assert(RegisterSize % LaneSize == 0);
- static_assert((NumLanes * LaneSize) % RegisterSize == 0);
-
- const int ideal = (NumLanes * LaneSize) / RegisterSize;
- if (ideal <= MaxRegisters)
- return ideal;
-
- // Look for the largest divisor of the ideal register count that is smaller than MaxRegisters
- for (int divisor = MaxRegisters; divisor > 1; --divisor)
- if (ideal % divisor == 0)
- return divisor;
-
- return 1;
- }
-
- static constexpr int NumRegs = BestRegisterCount<vec_t, WeightType, TransformedFeatureDimensions, NumRegistersSIMD>();
- static constexpr int NumPsqtRegs = BestRegisterCount<psqt_vec_t, PSQTWeightType, PSQTBuckets, NumRegistersSIMD>();
- #if defined(__GNUC__)
- #pragma GCC diagnostic pop
- #endif
- #endif
-
-
-
- // Input feature converter
- class FeatureTransformer {
+}
+ #define vec_load_psqt(a) _mm256_load_si256(a)
+ #define vec_store_psqt(a, b) _mm256_store_si256(a, b)
+ #define vec_add_psqt_32(a, b) _mm256_add_epi32(a, b)
+ #define vec_sub_psqt_32(a, b) _mm256_sub_epi32(a, b)
+ #define vec_zero_psqt() _mm256_setzero_si256()
+ #define NumRegistersSIMD 16
+ #define MaxChunkSize 32
+
+#elif USE_SSE2
+using vec_t = __m128i;
+using psqt_vec_t = __m128i;
+ #define vec_load(a) (*(a))
+ #define vec_store(a, b) *(a) = (b)
+ #define vec_add_16(a, b) _mm_add_epi16(a, b)
+ #define vec_sub_16(a, b) _mm_sub_epi16(a, b)
+ #define vec_mul_16(a, b) _mm_mullo_epi16(a, b)
+ #define vec_zero() _mm_setzero_si128()
+ #define vec_set_16(a) _mm_set1_epi16(a)
+ #define vec_max_16(a, b) _mm_max_epi16(a, b)
+ #define vec_min_16(a, b) _mm_min_epi16(a, b)
+ #define vec_msb_pack_16(a, b) _mm_packs_epi16(_mm_srli_epi16(a, 7), _mm_srli_epi16(b, 7))
+ #define vec_load_psqt(a) (*(a))
+ #define vec_store_psqt(a, b) *(a) = (b)
+ #define vec_add_psqt_32(a, b) _mm_add_epi32(a, b)
+ #define vec_sub_psqt_32(a, b) _mm_sub_epi32(a, b)
+ #define vec_zero_psqt() _mm_setzero_si128()
+ #define NumRegistersSIMD (Is64Bit ? 16 : 8)
+ #define MaxChunkSize 16
+
+#elif USE_NEON
+using vec_t = int16x8_t;
+using psqt_vec_t = int32x4_t;
+ #define vec_load(a) (*(a))
+ #define vec_store(a, b) *(a) = (b)
+ #define vec_add_16(a, b) vaddq_s16(a, b)
+ #define vec_sub_16(a, b) vsubq_s16(a, b)
+ #define vec_mul_16(a, b) vmulq_s16(a, b)
+ #define vec_zero() \
+ vec_t { 0 }
+ #define vec_set_16(a) vdupq_n_s16(a)
+ #define vec_max_16(a, b) vmaxq_s16(a, b)
+ #define vec_min_16(a, b) vminq_s16(a, b)
+inline vec_t vec_msb_pack_16(vec_t a, vec_t b) {
+ const int8x8_t shifta = vshrn_n_s16(a, 7);
+ const int8x8_t shiftb = vshrn_n_s16(b, 7);
+ const int8x16_t compacted = vcombine_s8(shifta, shiftb);
+ return *reinterpret_cast<const vec_t*>(&compacted);
+}
+ #define vec_load_psqt(a) (*(a))
+ #define vec_store_psqt(a, b) *(a) = (b)
+ #define vec_add_psqt_32(a, b) vaddq_s32(a, b)
+ #define vec_sub_psqt_32(a, b) vsubq_s32(a, b)
+ #define vec_zero_psqt() \
+ psqt_vec_t { 0 }
+ #define NumRegistersSIMD 16
+ #define MaxChunkSize 16
+
+#else
+ #undef VECTOR
+
+#endif
+
+
+#ifdef VECTOR
+
+ // Compute optimal SIMD register count for feature transformer accumulation.
+
+ // We use __m* types as template arguments, which causes GCC to emit warnings
+ // about losing some attribute information. This is irrelevant to us as we
+ // only take their size, so the following pragma are harmless.
+ #if defined(__GNUC__)
+ #pragma GCC diagnostic push
+ #pragma GCC diagnostic ignored "-Wignored-attributes"
+ #endif
+
+template<typename SIMDRegisterType, typename LaneType, int NumLanes, int MaxRegisters>
+static constexpr int BestRegisterCount() {
+ #define RegisterSize sizeof(SIMDRegisterType)
+ #define LaneSize sizeof(LaneType)
+
+ static_assert(RegisterSize >= LaneSize);
+ static_assert(MaxRegisters <= NumRegistersSIMD);
+ static_assert(MaxRegisters > 0);
+ static_assert(NumRegistersSIMD > 0);
+ static_assert(RegisterSize % LaneSize == 0);
+ static_assert((NumLanes * LaneSize) % RegisterSize == 0);
+
+ const int ideal = (NumLanes * LaneSize) / RegisterSize;
+ if (ideal <= MaxRegisters)
+ return ideal;
+
+ // Look for the largest divisor of the ideal register count that is smaller than MaxRegisters
+ for (int divisor = MaxRegisters; divisor > 1; --divisor)
+ if (ideal % divisor == 0)
+ return divisor;
+
+ return 1;
+}
+
+static constexpr int NumRegs =
+ BestRegisterCount<vec_t, WeightType, TransformedFeatureDimensions, NumRegistersSIMD>();
+static constexpr int NumPsqtRegs =
+ BestRegisterCount<psqt_vec_t, PSQTWeightType, PSQTBuckets, NumRegistersSIMD>();
+ #if defined(__GNUC__)
+ #pragma GCC diagnostic pop
+ #endif
+#endif
+
+
+// Input feature converter
+class FeatureTransformer {
private:
// Number of output dimensions for one side
static constexpr IndexType HalfDimensions = TransformedFeatureDimensions;
- #ifdef VECTOR
- static constexpr IndexType TileHeight = NumRegs * sizeof(vec_t) / 2;
+#ifdef VECTOR
+ static constexpr IndexType TileHeight = NumRegs * sizeof(vec_t) / 2;
static constexpr IndexType PsqtTileHeight = NumPsqtRegs * sizeof(psqt_vec_t) / 4;
static_assert(HalfDimensions % TileHeight == 0, "TileHeight must divide HalfDimensions");
static_assert(PSQTBuckets % PsqtTileHeight == 0, "PsqtTileHeight must divide PSQTBuckets");
- #endif
+#endif
public:
// Output type
using OutputType = TransformedFeatureType;
// Number of input/output dimensions
- static constexpr IndexType InputDimensions = FeatureSet::Dimensions;
+ static constexpr IndexType InputDimensions = FeatureSet::Dimensions;
static constexpr IndexType OutputDimensions = HalfDimensions;
// Size of forward propagation buffer
- static constexpr std::size_t BufferSize =
- OutputDimensions * sizeof(OutputType);
+ static constexpr std::size_t BufferSize = OutputDimensions * sizeof(OutputType);
// Hash value embedded in the evaluation file
static constexpr std::uint32_t get_hash_value() {
- return FeatureSet::HashValue ^ (OutputDimensions * 2);
+ return FeatureSet::HashValue ^ (OutputDimensions * 2);
}
// Read network parameters
bool read_parameters(std::istream& stream) {
- read_leb_128<BiasType >(stream, biases , HalfDimensions );
- read_leb_128<WeightType >(stream, weights , HalfDimensions * InputDimensions);
- read_leb_128<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
+ read_leb_128<BiasType>(stream, biases, HalfDimensions);
+ read_leb_128<WeightType>(stream, weights, HalfDimensions * InputDimensions);
+ read_leb_128<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
- return !stream.fail();
+ return !stream.fail();
}
// Write network parameters
bool write_parameters(std::ostream& stream) const {
- write_leb_128<BiasType >(stream, biases , HalfDimensions );
- write_leb_128<WeightType >(stream, weights , HalfDimensions * InputDimensions);
- write_leb_128<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
+ write_leb_128<BiasType>(stream, biases, HalfDimensions);
+ write_leb_128<WeightType>(stream, weights, HalfDimensions * InputDimensions);
+ write_leb_128<PSQTWeightType>(stream, psqtWeights, PSQTBuckets * InputDimensions);
- return !stream.fail();
+ return !stream.fail();
}
// Convert input features
std::int32_t transform(const Position& pos, OutputType* output, int bucket) const {
- update_accumulator<WHITE>(pos);
- update_accumulator<BLACK>(pos);
+ update_accumulator<WHITE>(pos);
+ update_accumulator<BLACK>(pos);
- const Color perspectives[2] = {pos.side_to_move(), ~pos.side_to_move()};
- const auto& accumulation = pos.state()->accumulator.accumulation;
- const auto& psqtAccumulation = pos.state()->accumulator.psqtAccumulation;
+ const Color perspectives[2] = {pos.side_to_move(), ~pos.side_to_move()};
+ const auto& accumulation = pos.state()->accumulator.accumulation;
+ const auto& psqtAccumulation = pos.state()->accumulator.psqtAccumulation;
- const auto psqt = (
- psqtAccumulation[perspectives[0]][bucket]
- - psqtAccumulation[perspectives[1]][bucket]
- ) / 2;
+ const auto psqt =
+ (psqtAccumulation[perspectives[0]][bucket] - psqtAccumulation[perspectives[1]][bucket])
+ / 2;
- for (IndexType p = 0; p < 2; ++p)
- {
- const IndexType offset = (HalfDimensions / 2) * p;
+ for (IndexType p = 0; p < 2; ++p)
+ {
+ const IndexType offset = (HalfDimensions / 2) * p;
#if defined(VECTOR)
- constexpr IndexType OutputChunkSize = MaxChunkSize;
- static_assert((HalfDimensions / 2) % OutputChunkSize == 0);
- constexpr IndexType NumOutputChunks = HalfDimensions / 2 / OutputChunkSize;
+ constexpr IndexType OutputChunkSize = MaxChunkSize;
+ static_assert((HalfDimensions / 2) % OutputChunkSize == 0);
+ constexpr IndexType NumOutputChunks = HalfDimensions / 2 / OutputChunkSize;
- vec_t Zero = vec_zero();
- vec_t One = vec_set_16(127);
+ vec_t Zero = vec_zero();
+ vec_t One = vec_set_16(127);
- const vec_t* in0 = reinterpret_cast<const vec_t*>(&(accumulation[perspectives[p]][0]));
- const vec_t* in1 = reinterpret_cast<const vec_t*>(&(accumulation[perspectives[p]][HalfDimensions / 2]));
- vec_t* out = reinterpret_cast< vec_t*>(output + offset);
+ const vec_t* in0 = reinterpret_cast<const vec_t*>(&(accumulation[perspectives[p]][0]));
+ const vec_t* in1 =
+ reinterpret_cast<const vec_t*>(&(accumulation[perspectives[p]][HalfDimensions / 2]));
+ vec_t* out = reinterpret_cast<vec_t*>(output + offset);
- for (IndexType j = 0; j < NumOutputChunks; j += 1)
- {
- const vec_t sum0a = vec_max_16(vec_min_16(in0[j * 2 + 0], One), Zero);
- const vec_t sum0b = vec_max_16(vec_min_16(in0[j * 2 + 1], One), Zero);
- const vec_t sum1a = vec_max_16(vec_min_16(in1[j * 2 + 0], One), Zero);
- const vec_t sum1b = vec_max_16(vec_min_16(in1[j * 2 + 1], One), Zero);
+ for (IndexType j = 0; j < NumOutputChunks; j += 1)
+ {
+ const vec_t sum0a = vec_max_16(vec_min_16(in0[j * 2 + 0], One), Zero);
+ const vec_t sum0b = vec_max_16(vec_min_16(in0[j * 2 + 1], One), Zero);
+ const vec_t sum1a = vec_max_16(vec_min_16(in1[j * 2 + 0], One), Zero);
+ const vec_t sum1b = vec_max_16(vec_min_16(in1[j * 2 + 1], One), Zero);
- const vec_t pa = vec_mul_16(sum0a, sum1a);
- const vec_t pb = vec_mul_16(sum0b, sum1b);
+ const vec_t pa = vec_mul_16(sum0a, sum1a);
+ const vec_t pb = vec_mul_16(sum0b, sum1b);
- out[j] = vec_msb_pack_16(pa, pb);
- }
+ out[j] = vec_msb_pack_16(pa, pb);
+ }
#else
- for (IndexType j = 0; j < HalfDimensions / 2; ++j) {
- BiasType sum0 = accumulation[static_cast<int>(perspectives[p])][j + 0];
- BiasType sum1 = accumulation[static_cast<int>(perspectives[p])][j + HalfDimensions / 2];
- sum0 = std::max<int>(0, std::min<int>(127, sum0));
- sum1 = std::max<int>(0, std::min<int>(127, sum1));
- output[offset + j] = static_cast<OutputType>(sum0 * sum1 / 128);
- }
-
-#endif
- }
+ for (IndexType j = 0; j < HalfDimensions / 2; ++j)
+ {
+ BiasType sum0 = accumulation[static_cast<int>(perspectives[p])][j + 0];
+ BiasType sum1 =
+ accumulation[static_cast<int>(perspectives[p])][j + HalfDimensions / 2];
+ sum0 = std::clamp<BiasType>(sum0, 0, 127);
+ sum1 = std::clamp<BiasType>(sum1, 0, 127);
+ output[offset + j] = static_cast<OutputType>(unsigned(sum0 * sum1) / 128);
+ }
-#if defined(vec_cleanup)
- vec_cleanup();
#endif
+ }
- return psqt;
- } // end of function transform()
+ return psqt;
+ } // end of function transform()
void hint_common_access(const Position& pos) const {
- hint_common_access_for_perspective<WHITE>(pos);
- hint_common_access_for_perspective<BLACK>(pos);
+ hint_common_access_for_perspective<WHITE>(pos);
+ hint_common_access_for_perspective<BLACK>(pos);
}
private:
template<Color Perspective>
- [[nodiscard]] std::pair<StateInfo*, StateInfo*> try_find_computed_accumulator(const Position& pos) const {
- // Look for a usable accumulator of an earlier position. We keep track
- // of the estimated gain in terms of features to be added/subtracted.
- StateInfo *st = pos.state(), *next = nullptr;
- int gain = FeatureSet::refresh_cost(pos);
- while (st->previous && !st->accumulator.computed[Perspective])
- {
- // This governs when a full feature refresh is needed and how many
- // updates are better than just one full refresh.
- if ( FeatureSet::requires_refresh(st, Perspective)
- || (gain -= FeatureSet::update_cost(st) + 1) < 0)
- break;
- next = st;
- st = st->previous;
- }
- return { st, next };
+ [[nodiscard]] std::pair<StateInfo*, StateInfo*>
+ try_find_computed_accumulator(const Position& pos) const {
+ // Look for a usable accumulator of an earlier position. We keep track
+ // of the estimated gain in terms of features to be added/subtracted.
+ StateInfo *st = pos.state(), *next = nullptr;
+ int gain = FeatureSet::refresh_cost(pos);
+ while (st->previous && !st->accumulator.computed[Perspective])
+ {
+ // This governs when a full feature refresh is needed and how many
+ // updates are better than just one full refresh.
+ if (FeatureSet::requires_refresh(st, Perspective)
+ || (gain -= FeatureSet::update_cost(st) + 1) < 0)
+ break;
+ next = st;
+ st = st->previous;
+ }
+ return {st, next};
}
// NOTE: The parameter states_to_update is an array of position states, ending with nullptr.
// All states must be sequential, that is states_to_update[i] must either be reachable
- // by repeatedly applying ->previous from states_to_update[i+1] or states_to_update[i] == nullptr.
- // computed_st must be reachable by repeatedly applying ->previous on states_to_update[0], if not nullptr.
+ // by repeatedly applying ->previous from states_to_update[i+1] or
+ // states_to_update[i] == nullptr.
+ // computed_st must be reachable by repeatedly applying ->previous on
+ // states_to_update[0], if not nullptr.
template<Color Perspective, size_t N>
- void update_accumulator_incremental(const Position& pos, StateInfo* computed_st, StateInfo* states_to_update[N]) const {
- static_assert(N > 0);
- assert(states_to_update[N-1] == nullptr);
+ void update_accumulator_incremental(const Position& pos,
+ StateInfo* computed_st,
+ StateInfo* states_to_update[N]) const {
+ static_assert(N > 0);
+ assert(states_to_update[N - 1] == nullptr);
- #ifdef VECTOR
- // Gcc-10.2 unnecessarily spills AVX2 registers if this array
- // is defined in the VECTOR code below, once in each branch
- vec_t acc[NumRegs];
- psqt_vec_t psqt[NumPsqtRegs];
- #endif
+#ifdef VECTOR
+ // Gcc-10.2 unnecessarily spills AVX2 registers if this array
+ // is defined in the VECTOR code below, once in each branch
+ vec_t acc[NumRegs];
+ psqt_vec_t psqt[NumPsqtRegs];
+#endif
- if (states_to_update[0] == nullptr)
- return;
+ if (states_to_update[0] == nullptr)
+ return;
- // Update incrementally going back through states_to_update.
+ // Update incrementally going back through states_to_update.
- // Gather all features to be updated.
- const Square ksq = pos.square<KING>(Perspective);
+ // Gather all features to be updated.
+ const Square ksq = pos.square<KING>(Perspective);
- // The size must be enough to contain the largest possible update.
- // That might depend on the feature set and generally relies on the
- // feature set's update cost calculation to be correct and never
- // allow updates with more added/removed features than MaxActiveDimensions.
- FeatureSet::IndexList removed[N-1], added[N-1];
+ // The size must be enough to contain the largest possible update.
+ // That might depend on the feature set and generally relies on the
+ // feature set's update cost calculation to be correct and never
+ // allow updates with more added/removed features than MaxActiveDimensions.
+ FeatureSet::IndexList removed[N - 1], added[N - 1];
- {
- int i = N-2; // last potential state to update. Skip last element because it must be nullptr.
- while (states_to_update[i] == nullptr)
- --i;
+ {
+ int i =
+ N
+ - 2; // last potential state to update. Skip last element because it must be nullptr.
+ while (states_to_update[i] == nullptr)
+ --i;
- StateInfo *st2 = states_to_update[i];
+ StateInfo* st2 = states_to_update[i];
- for (; i >= 0; --i)
- {
- states_to_update[i]->accumulator.computed[Perspective] = true;
+ for (; i >= 0; --i)
+ {
+ states_to_update[i]->accumulator.computed[Perspective] = true;
- StateInfo* end_state = i == 0 ? computed_st : states_to_update[i - 1];
+ const StateInfo* end_state = i == 0 ? computed_st : states_to_update[i - 1];
- for (; st2 != end_state; st2 = st2->previous)
- FeatureSet::append_changed_indices<Perspective>(
- ksq, st2->dirtyPiece, removed[i], added[i]);
+ for (; st2 != end_state; st2 = st2->previous)
+ FeatureSet::append_changed_indices<Perspective>(ksq, st2->dirtyPiece,
+ removed[i], added[i]);
+ }
}
- }
- StateInfo* st = computed_st;
+ StateInfo* st = computed_st;
- // Now update the accumulators listed in states_to_update[], where the last element is a sentinel.
+ // Now update the accumulators listed in states_to_update[], where the last element is a sentinel.
#ifdef VECTOR
- for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
- {
- // Load accumulator
- auto accTile = reinterpret_cast<vec_t*>(
- &st->accumulator.accumulation[Perspective][j * TileHeight]);
- for (IndexType k = 0; k < NumRegs; ++k)
- acc[k] = vec_load(&accTile[k]);
- for (IndexType i = 0; states_to_update[i]; ++i)
+ if (states_to_update[1] == nullptr && (removed[0].size() == 1 || removed[0].size() == 2)
+ && added[0].size() == 1)
{
- // Difference calculation for the deactivated features
- for (const auto index : removed[i])
- {
- const IndexType offset = HalfDimensions * index + j * TileHeight;
- auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
- for (IndexType k = 0; k < NumRegs; ++k)
- acc[k] = vec_sub_16(acc[k], column[k]);
- }
-
- // Difference calculation for the activated features
- for (const auto index : added[i])
- {
- const IndexType offset = HalfDimensions * index + j * TileHeight;
- auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
- for (IndexType k = 0; k < NumRegs; ++k)
- acc[k] = vec_add_16(acc[k], column[k]);
- }
-
- // Store accumulator
- accTile = reinterpret_cast<vec_t*>(
- &states_to_update[i]->accumulator.accumulation[Perspective][j * TileHeight]);
- for (IndexType k = 0; k < NumRegs; ++k)
- vec_store(&accTile[k], acc[k]);
+ assert(states_to_update[0]);
+
+ auto accIn =
+ reinterpret_cast<const vec_t*>(&st->accumulator.accumulation[Perspective][0]);
+ auto accOut = reinterpret_cast<vec_t*>(
+ &states_to_update[0]->accumulator.accumulation[Perspective][0]);
+
+ const IndexType offsetR0 = HalfDimensions * removed[0][0];
+ auto columnR0 = reinterpret_cast<const vec_t*>(&weights[offsetR0]);
+ const IndexType offsetA = HalfDimensions * added[0][0];
+ auto columnA = reinterpret_cast<const vec_t*>(&weights[offsetA]);
+
+ if (removed[0].size() == 1)
+ {
+ for (IndexType k = 0; k < HalfDimensions * sizeof(std::int16_t) / sizeof(vec_t);
+ ++k)
+ accOut[k] = vec_add_16(vec_sub_16(accIn[k], columnR0[k]), columnA[k]);
+ }
+ else
+ {
+ const IndexType offsetR1 = HalfDimensions * removed[0][1];
+ auto columnR1 = reinterpret_cast<const vec_t*>(&weights[offsetR1]);
+
+ for (IndexType k = 0; k < HalfDimensions * sizeof(std::int16_t) / sizeof(vec_t);
+ ++k)
+ accOut[k] = vec_sub_16(vec_add_16(accIn[k], columnA[k]),
+ vec_add_16(columnR0[k], columnR1[k]));
+ }
+
+ auto accPsqtIn = reinterpret_cast<const psqt_vec_t*>(
+ &st->accumulator.psqtAccumulation[Perspective][0]);
+ auto accPsqtOut = reinterpret_cast<psqt_vec_t*>(
+ &states_to_update[0]->accumulator.psqtAccumulation[Perspective][0]);
+
+ const IndexType offsetPsqtR0 = PSQTBuckets * removed[0][0];
+ auto columnPsqtR0 = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offsetPsqtR0]);
+ const IndexType offsetPsqtA = PSQTBuckets * added[0][0];
+ auto columnPsqtA = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offsetPsqtA]);
+
+ if (removed[0].size() == 1)
+ {
+ for (std::size_t k = 0; k < PSQTBuckets * sizeof(std::int32_t) / sizeof(psqt_vec_t);
+ ++k)
+ accPsqtOut[k] = vec_add_psqt_32(vec_sub_psqt_32(accPsqtIn[k], columnPsqtR0[k]),
+ columnPsqtA[k]);
+ }
+ else
+ {
+ const IndexType offsetPsqtR1 = PSQTBuckets * removed[0][1];
+ auto columnPsqtR1 = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offsetPsqtR1]);
+
+ for (std::size_t k = 0; k < PSQTBuckets * sizeof(std::int32_t) / sizeof(psqt_vec_t);
+ ++k)
+ accPsqtOut[k] =
+ vec_sub_psqt_32(vec_add_psqt_32(accPsqtIn[k], columnPsqtA[k]),
+ vec_add_psqt_32(columnPsqtR0[k], columnPsqtR1[k]));
+ }
}
- }
-
- for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
- {
- // Load accumulator
- auto accTilePsqt = reinterpret_cast<psqt_vec_t*>(
- &st->accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
- for (std::size_t k = 0; k < NumPsqtRegs; ++k)
- psqt[k] = vec_load_psqt(&accTilePsqt[k]);
-
- for (IndexType i = 0; states_to_update[i]; ++i)
+ else
{
- // Difference calculation for the deactivated features
- for (const auto index : removed[i])
- {
- const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
- auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
- for (std::size_t k = 0; k < NumPsqtRegs; ++k)
- psqt[k] = vec_sub_psqt_32(psqt[k], columnPsqt[k]);
- }
-
- // Difference calculation for the activated features
- for (const auto index : added[i])
- {
- const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
- auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
- for (std::size_t k = 0; k < NumPsqtRegs; ++k)
- psqt[k] = vec_add_psqt_32(psqt[k], columnPsqt[k]);
- }
-
- // Store accumulator
- accTilePsqt = reinterpret_cast<psqt_vec_t*>(
- &states_to_update[i]->accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
- for (std::size_t k = 0; k < NumPsqtRegs; ++k)
- vec_store_psqt(&accTilePsqt[k], psqt[k]);
+ for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
+ {
+ // Load accumulator
+ auto accTileIn = reinterpret_cast<const vec_t*>(
+ &st->accumulator.accumulation[Perspective][j * TileHeight]);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ acc[k] = vec_load(&accTileIn[k]);
+
+ for (IndexType i = 0; states_to_update[i]; ++i)
+ {
+ // Difference calculation for the deactivated features
+ for (const auto index : removed[i])
+ {
+ const IndexType offset = HalfDimensions * index + j * TileHeight;
+ auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ acc[k] = vec_sub_16(acc[k], column[k]);
+ }
+
+ // Difference calculation for the activated features
+ for (const auto index : added[i])
+ {
+ const IndexType offset = HalfDimensions * index + j * TileHeight;
+ auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ acc[k] = vec_add_16(acc[k], column[k]);
+ }
+
+ // Store accumulator
+ auto accTileOut = reinterpret_cast<vec_t*>(
+ &states_to_update[i]->accumulator.accumulation[Perspective][j * TileHeight]);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ vec_store(&accTileOut[k], acc[k]);
+ }
+ }
+
+ for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
+ {
+ // Load accumulator
+ auto accTilePsqtIn = reinterpret_cast<const psqt_vec_t*>(
+ &st->accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
+ for (std::size_t k = 0; k < NumPsqtRegs; ++k)
+ psqt[k] = vec_load_psqt(&accTilePsqtIn[k]);
+
+ for (IndexType i = 0; states_to_update[i]; ++i)
+ {
+ // Difference calculation for the deactivated features
+ for (const auto index : removed[i])
+ {
+ const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
+ auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
+ for (std::size_t k = 0; k < NumPsqtRegs; ++k)
+ psqt[k] = vec_sub_psqt_32(psqt[k], columnPsqt[k]);
+ }
+
+ // Difference calculation for the activated features
+ for (const auto index : added[i])
+ {
+ const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
+ auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
+ for (std::size_t k = 0; k < NumPsqtRegs; ++k)
+ psqt[k] = vec_add_psqt_32(psqt[k], columnPsqt[k]);
+ }
+
+ // Store accumulator
+ auto accTilePsqtOut = reinterpret_cast<psqt_vec_t*>(
+ &states_to_update[i]
+ ->accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
+ for (std::size_t k = 0; k < NumPsqtRegs; ++k)
+ vec_store_psqt(&accTilePsqtOut[k], psqt[k]);
+ }
+ }
}
- }
-
#else
- for (IndexType i = 0; states_to_update[i]; ++i)
- {
- std::memcpy(states_to_update[i]->accumulator.accumulation[Perspective],
- st->accumulator.accumulation[Perspective],
- HalfDimensions * sizeof(BiasType));
+ for (IndexType i = 0; states_to_update[i]; ++i)
+ {
+ std::memcpy(states_to_update[i]->accumulator.accumulation[Perspective],
+ st->accumulator.accumulation[Perspective],
+ HalfDimensions * sizeof(BiasType));
- for (std::size_t k = 0; k < PSQTBuckets; ++k)
- states_to_update[i]->accumulator.psqtAccumulation[Perspective][k] = st->accumulator.psqtAccumulation[Perspective][k];
+ for (std::size_t k = 0; k < PSQTBuckets; ++k)
+ states_to_update[i]->accumulator.psqtAccumulation[Perspective][k] =
+ st->accumulator.psqtAccumulation[Perspective][k];
- st = states_to_update[i];
+ st = states_to_update[i];
- // Difference calculation for the deactivated features
- for (const auto index : removed[i])
- {
- const IndexType offset = HalfDimensions * index;
+ // Difference calculation for the deactivated features
+ for (const auto index : removed[i])
+ {
+ const IndexType offset = HalfDimensions * index;
- for (IndexType j = 0; j < HalfDimensions; ++j)
- st->accumulator.accumulation[Perspective][j] -= weights[offset + j];
+ for (IndexType j = 0; j < HalfDimensions; ++j)
+ st->accumulator.accumulation[Perspective][j] -= weights[offset + j];
- for (std::size_t k = 0; k < PSQTBuckets; ++k)
- st->accumulator.psqtAccumulation[Perspective][k] -= psqtWeights[index * PSQTBuckets + k];
- }
+ for (std::size_t k = 0; k < PSQTBuckets; ++k)
+ st->accumulator.psqtAccumulation[Perspective][k] -=
+ psqtWeights[index * PSQTBuckets + k];
+ }
- // Difference calculation for the activated features
- for (const auto index : added[i])
- {
- const IndexType offset = HalfDimensions * index;
+ // Difference calculation for the activated features
+ for (const auto index : added[i])
+ {
+ const IndexType offset = HalfDimensions * index;
- for (IndexType j = 0; j < HalfDimensions; ++j)
- st->accumulator.accumulation[Perspective][j] += weights[offset + j];
+ for (IndexType j = 0; j < HalfDimensions; ++j)
+ st->accumulator.accumulation[Perspective][j] += weights[offset + j];
- for (std::size_t k = 0; k < PSQTBuckets; ++k)
- st->accumulator.psqtAccumulation[Perspective][k] += psqtWeights[index * PSQTBuckets + k];
+ for (std::size_t k = 0; k < PSQTBuckets; ++k)
+ st->accumulator.psqtAccumulation[Perspective][k] +=
+ psqtWeights[index * PSQTBuckets + k];
+ }
}
- }
#endif
-
- #if defined(USE_MMX)
- _mm_empty();
- #endif
}
template<Color Perspective>
void update_accumulator_refresh(const Position& pos) const {
- #ifdef VECTOR
- // Gcc-10.2 unnecessarily spills AVX2 registers if this array
- // is defined in the VECTOR code below, once in each branch
- vec_t acc[NumRegs];
- psqt_vec_t psqt[NumPsqtRegs];
- #endif
-
- // Refresh the accumulator
- // Could be extracted to a separate function because it's done in 2 places,
- // but it's unclear if compilers would correctly handle register allocation.
- auto& accumulator = pos.state()->accumulator;
- accumulator.computed[Perspective] = true;
- FeatureSet::IndexList active;
- FeatureSet::append_active_indices<Perspective>(pos, active);
-
#ifdef VECTOR
- for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
- {
- auto biasesTile = reinterpret_cast<const vec_t*>(
- &biases[j * TileHeight]);
- for (IndexType k = 0; k < NumRegs; ++k)
- acc[k] = biasesTile[k];
+ // Gcc-10.2 unnecessarily spills AVX2 registers if this array
+ // is defined in the VECTOR code below, once in each branch
+ vec_t acc[NumRegs];
+ psqt_vec_t psqt[NumPsqtRegs];
+#endif
- for (const auto index : active)
+ // Refresh the accumulator
+ // Could be extracted to a separate function because it's done in 2 places,
+ // but it's unclear if compilers would correctly handle register allocation.
+ auto& accumulator = pos.state()->accumulator;
+ accumulator.computed[Perspective] = true;
+ FeatureSet::IndexList active;
+ FeatureSet::append_active_indices<Perspective>(pos, active);
+
+#ifdef VECTOR
+ for (IndexType j = 0; j < HalfDimensions / TileHeight; ++j)
{
- const IndexType offset = HalfDimensions * index + j * TileHeight;
- auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
+ auto biasesTile = reinterpret_cast<const vec_t*>(&biases[j * TileHeight]);
+ for (IndexType k = 0; k < NumRegs; ++k)
+ acc[k] = biasesTile[k];
- for (unsigned k = 0; k < NumRegs; ++k)
- acc[k] = vec_add_16(acc[k], column[k]);
- }
+ for (const auto index : active)
+ {
+ const IndexType offset = HalfDimensions * index + j * TileHeight;
+ auto column = reinterpret_cast<const vec_t*>(&weights[offset]);
- auto accTile = reinterpret_cast<vec_t*>(
- &accumulator.accumulation[Perspective][j * TileHeight]);
- for (unsigned k = 0; k < NumRegs; k++)
- vec_store(&accTile[k], acc[k]);
- }
+ for (unsigned k = 0; k < NumRegs; ++k)
+ acc[k] = vec_add_16(acc[k], column[k]);
+ }
- for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
- {
- for (std::size_t k = 0; k < NumPsqtRegs; ++k)
- psqt[k] = vec_zero_psqt();
+ auto accTile =
+ reinterpret_cast<vec_t*>(&accumulator.accumulation[Perspective][j * TileHeight]);
+ for (unsigned k = 0; k < NumRegs; k++)
+ vec_store(&accTile[k], acc[k]);
+ }
- for (const auto index : active)
+ for (IndexType j = 0; j < PSQTBuckets / PsqtTileHeight; ++j)
{
- const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
- auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
+ for (std::size_t k = 0; k < NumPsqtRegs; ++k)
+ psqt[k] = vec_zero_psqt();
- for (std::size_t k = 0; k < NumPsqtRegs; ++k)
- psqt[k] = vec_add_psqt_32(psqt[k], columnPsqt[k]);
- }
+ for (const auto index : active)
+ {
+ const IndexType offset = PSQTBuckets * index + j * PsqtTileHeight;
+ auto columnPsqt = reinterpret_cast<const psqt_vec_t*>(&psqtWeights[offset]);
- auto accTilePsqt = reinterpret_cast<psqt_vec_t*>(
- &accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
- for (std::size_t k = 0; k < NumPsqtRegs; ++k)
- vec_store_psqt(&accTilePsqt[k], psqt[k]);
- }
+ for (std::size_t k = 0; k < NumPsqtRegs; ++k)
+ psqt[k] = vec_add_psqt_32(psqt[k], columnPsqt[k]);
+ }
+
+ auto accTilePsqt = reinterpret_cast<psqt_vec_t*>(
+ &accumulator.psqtAccumulation[Perspective][j * PsqtTileHeight]);
+ for (std::size_t k = 0; k < NumPsqtRegs; ++k)
+ vec_store_psqt(&accTilePsqt[k], psqt[k]);
+ }
#else
- std::memcpy(accumulator.accumulation[Perspective], biases,
- HalfDimensions * sizeof(BiasType));
+ std::memcpy(accumulator.accumulation[Perspective], biases,
+ HalfDimensions * sizeof(BiasType));
- for (std::size_t k = 0; k < PSQTBuckets; ++k)
- accumulator.psqtAccumulation[Perspective][k] = 0;
+ for (std::size_t k = 0; k < PSQTBuckets; ++k)
+ accumulator.psqtAccumulation[Perspective][k] = 0;
- for (const auto index : active)
- {
- const IndexType offset = HalfDimensions * index;
+ for (const auto index : active)
+ {
+ const IndexType offset = HalfDimensions * index;
- for (IndexType j = 0; j < HalfDimensions; ++j)
- accumulator.accumulation[Perspective][j] += weights[offset + j];
+ for (IndexType j = 0; j < HalfDimensions; ++j)
+ accumulator.accumulation[Perspective][j] += weights[offset + j];
- for (std::size_t k = 0; k < PSQTBuckets; ++k)
- accumulator.psqtAccumulation[Perspective][k] += psqtWeights[index * PSQTBuckets + k];
- }
+ for (std::size_t k = 0; k < PSQTBuckets; ++k)
+ accumulator.psqtAccumulation[Perspective][k] +=
+ psqtWeights[index * PSQTBuckets + k];
+ }
#endif
-
- #if defined(USE_MMX)
- _mm_empty();
- #endif
}
template<Color Perspective>
void hint_common_access_for_perspective(const Position& pos) const {
- // Works like update_accumulator, but performs less work.
- // Updates ONLY the accumulator for pos.
-
- // Look for a usable accumulator of an earlier position. We keep track
- // of the estimated gain in terms of features to be added/subtracted.
- // Fast early exit.
- if (pos.state()->accumulator.computed[Perspective])
- return;
-
- auto [oldest_st, _] = try_find_computed_accumulator<Perspective>(pos);
-
- if (oldest_st->accumulator.computed[Perspective])
- {
- // Only update current position accumulator to minimize work.
- StateInfo* states_to_update[2] = { pos.state(), nullptr };
- update_accumulator_incremental<Perspective, 2>(pos, oldest_st, states_to_update);
- }
- else
- {
- update_accumulator_refresh<Perspective>(pos);
- }
+ // Works like update_accumulator, but performs less work.
+ // Updates ONLY the accumulator for pos.
+
+ // Look for a usable accumulator of an earlier position. We keep track
+ // of the estimated gain in terms of features to be added/subtracted.
+ // Fast early exit.
+ if (pos.state()->accumulator.computed[Perspective])
+ return;
+
+
auto [oldest_st, _] = try_find_computed_accumulator<Perspective>(pos);
+
+ if (oldest_st->accumulator.computed[Perspective])
+ {
+ // Only update current position accumulator to minimize work.
+ StateInfo* states_to_update[2] = {pos.state(), nullptr};
+
update_accumulator_incremental<Perspective, 2>(pos, oldest_st, states_to_update);
+ }
+ else
+ {
+
update_accumulator_refresh<Perspective>(pos);
+ }
}
template<Color Perspective>
void update_accumulator(const Position& pos) const {
- auto [oldest_st, next] = try_find_computed_accumulator<Perspective>(pos);
-
- if (oldest_st->accumulator.computed[Perspective])
- {
- if (next == nullptr)
- return;
+ auto [oldest_st, next] = try_find_computed_accumulator<Perspective>(pos);
- // Now update the accumulators listed in states_to_update[], where the last element is a sentinel.
- // Currently we update 2 accumulators.
- // 1. for the current position
- // 2. the next accumulator after the computed one
- // The heuristic may change in the future.
- StateInfo *states_to_update[3] =
- { next, next == pos.state() ? nullptr : pos.state(), nullptr };
-
- update_accumulator_incremental<Perspective, 3>(pos, oldest_st, states_to_update);
- }
- else
- {
- update_accumulator_refresh<Perspective>(pos);
- }
+ if (oldest_st->accumulator.computed[Perspective])
+ {
+ if (next == nullptr)
+ return;
+
+ // Now update the accumulators listed in states_to_update[], where the last element is a sentinel.
+ // Currently we update 2 accumulators.
+ // 1. for the current position
+ // 2. the next accumulator after the computed one
+ // The heuristic may change in the future.
+ StateInfo* states_to_update[3] = {next, next == pos.state() ? nullptr : pos.state(),
+ nullptr};
+
+ update_accumulator_incremental<Perspective, 3>(pos, oldest_st, states_to_update);
+ }
+ else
+ {
+ update_accumulator_refresh<Perspective>(pos);
+ }
}
alignas(CacheLineSize) BiasType biases[HalfDimensions];
alignas(CacheLineSize) WeightType weights[HalfDimensions * InputDimensions];
alignas(CacheLineSize) PSQTWeightType psqtWeights[InputDimensions * PSQTBuckets];
- };
+};
} // namespace Stockfish::Eval::NNUE
-#endif // #ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED
+#endif // #ifndef NNUE_FEATURE_TRANSFORMER_H_INCLUDED
projects / stockfish / blobdiff